Media management optimization for transaction terminals

A method, comprising:

• receiving, by a first machine learning model (MLM) executing on a processor of a cloud server, historical transaction and media data associated with terminals of a store;
• training, by the first MLM executing on the processor of the cloud server, based on the historical transaction and media data to generate optimal media baselines for the terminals;
• receiving, by a second MLM executing on the processor of the cloud server, the optimal media baselines from the first MLM;
• generating, by the second MLM, an optimized cash management schedule based at least in part on the optimal media baselines;
• and
• providing, through an application programming interface (API), the optimized cash management schedule to a service or a system of the store.

A system, comprising:

• a cloud server comprising at least one processor and a non-transitory computer-readable storage medium;
• the non-transitory computer-readable storage medium comprises executable instructions;
• the executable instructions when provided to and executed by the at least one processor from the non-transitory computer-readable storage medium cause the at least one processor to perform operations comprising:
• training a media baseline machine learning model (MLM) based on historical transaction and media data to generate optimal media baselines for transaction terminals of a store;
• training a media action scheduling MLM on historical data and the optimal media baselines to generate cash management recommendations;
• receiving real-time transaction data from the transaction terminals;
• generating, by the media action scheduling MLM, an optimized cash management schedule based on the cash management recommendations and the real-time transaction data;
• and
• providing, through an application programming interface (API), the optimized cash management schedule to a service or a system of the store.